关键词: 消息传递接口集群, 粒子群优化算法, K均值算法, 并行聚类

Abstract: The performance of traditional serial clustering algorithm cannot meet the needs of data clustering of the huge amounts of data. To enhance the performance of clustering algorithm, a new clustering algorithm combining parallel Particle Swarm Optimization (PSO) with K-means based on MPI was proposed in this paper. Firstly, the improved PSO was combined with K-means to enhance the capacity of global search, and then a new parallel clustering algorithm was proposed, which was compared with K-means and PSO clustering algorithms. The experimental results show that the new algorithm has better global convergence, and also has higher speed-up ratio.

Key words: Message Passing Interface (MPI) cluster, Particle Swarm Optimization (PSO) algorithm, K-means algorithm, parallel clustering